Content:

Introduction

This analysis is about the quality of red wine, the varieties and involving their chemical properties as well as the ranking by tasters. The pricing of wine depends on a rather abstract concept of wine appreciation by wine testers, opinion among whom may have a high degree of variablitly. Another key factor is in wine certification and quality assessment are physiochemical tests, laboratory based. It takes into account for example the ph-score and chlorides amongst others. One interesting question in this context is, if there is a correlation between chemical properties and the human taste.

(source: Penn State Eberly College of Science)

What are we going to do tonight, Brain?
Same thing we do every night, Pinky.

oh, veyh Pinky and Brain are analysing the red wine data and plot how to rule the world with red wine???

pbbottle.

pbbottle.

Objective of the analysis will be to predict the Quality Ranking from the chemical properties of the red wine using explanatory data analysis (EDA) to explore the relationships between the variables: visualisation, distributions, outliers and potential anomalies. This project is prepared with R Studio.

The dataset contains 13 variables and 1599 observations.

Description of the variables (based on pysiochemical testing)

fixed acidity: most acids involved with wine volatile acidity: the amount of acetic acid, very high levels can lead to unpleasant taste (vinegar like ) citric acid: can add ‘freshness’ to the wine residual sugar: amount of sugar after fermentation stops chlorides: amount of salt free sulfur dioxide: free form of SO2, prevents microbiological growth and oxidation total sulfur dioxide: amount of free and bound SO2, becomes evident in nose and taste density: water depending on the percent of alcohol ph: describes how acid or basic the wine is sulphates: additive, antimicrobial and antioxidant alcohol: percent of alcohol quality: output variable, sensoric data

library(ggplot2)

This first summary gives us a first insight. The range of possible socres is 0 to ten. The min in our data set is 3, the max is 8, the median is 6 and the mean is 5.64.

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   3.000   5.000   6.000   5.636   6.000   8.000

The summary function is giving us as well some good information on the diffrent variable data.

##  fixed.acidity   volatile.acidity  citric.acid    residual.sugar  
##  Min.   : 4.60   Min.   :0.1200   Min.   :0.000   Min.   : 0.900  
##  1st Qu.: 7.10   1st Qu.:0.3900   1st Qu.:0.090   1st Qu.: 1.900  
##  Median : 7.90   Median :0.5200   Median :0.260   Median : 2.200  
##  Mean   : 8.32   Mean   :0.5278   Mean   :0.271   Mean   : 2.539  
##  3rd Qu.: 9.20   3rd Qu.:0.6400   3rd Qu.:0.420   3rd Qu.: 2.600  
##  Max.   :15.90   Max.   :1.5800   Max.   :1.000   Max.   :15.500  
##    chlorides       free.sulfur.dioxide total.sulfur.dioxide
##  Min.   :0.01200   Min.   : 1.00       Min.   :  6.00      
##  1st Qu.:0.07000   1st Qu.: 7.00       1st Qu.: 22.00      
##  Median :0.07900   Median :14.00       Median : 38.00      
##  Mean   :0.08747   Mean   :15.87       Mean   : 46.47      
##  3rd Qu.:0.09000   3rd Qu.:21.00       3rd Qu.: 62.00      
##  Max.   :0.61100   Max.   :72.00       Max.   :289.00      
##     density             pH          sulphates         alcohol     
##  Min.   :0.9901   Min.   :2.740   Min.   :0.3300   Min.   : 8.40  
##  1st Qu.:0.9956   1st Qu.:3.210   1st Qu.:0.5500   1st Qu.: 9.50  
##  Median :0.9968   Median :3.310   Median :0.6200   Median :10.20  
##  Mean   :0.9967   Mean   :3.311   Mean   :0.6581   Mean   :10.42  
##  3rd Qu.:0.9978   3rd Qu.:3.400   3rd Qu.:0.7300   3rd Qu.:11.10  
##  Max.   :1.0037   Max.   :4.010   Max.   :2.0000   Max.   :14.90  
##     quality     
##  Min.   :3.000  
##  1st Qu.:5.000  
##  Median :6.000  
##  Mean   :5.636  
##  3rd Qu.:6.000  
##  Max.   :8.000

The first variable I want to check on is the quality as for me it is the first criterion.

The histogramm shows normal distributed data for the variable quality. As we have seen before the median is 6 and the mean is 5.64 which is really near to the median and the graph perfectly reflects this analysis.

I think the Quality can be used to make a rating variable.

I am using qplot here as the variable for rating is discrete.

Most of the wines have an average quality, but I decide to make a pie chart which might look prettier than this simple qplot histogramm.

The pie chart is showing that the most of the wines are rated avarage, not so many are bad and there are more good wines than bad ones.

In the next steps I am going to investigate more deeply on the different variables and will check if there are outliers.

Univariate Plotting

analysis.

analysis.

Pinky is beginning the analysis on the different variables. What will be the outcome?

With the graphs I want to check on the distribution of the variables. I decide to keep the grid as it is a good first insight. Next step is to check on the other variables, their distribution and potentially there could be outliers discovered.

The alcohol histogramm is positively skewed and there is a tiny amount of outliers. The peak is around 9 % alcohol content. Mean and median are close: 10.20 - 10.42

Fixed acidy appeared positively skewed and we know a big chunk of the data fall between 7 and 9. there seem to be an amount of high outliers.

The distribution of Volatile acidity looks like Bimodal with some outliers.

Apart from one outliers, the distribution of Citric acid looks strange. It is not easy to describe this distribution, as there are several peaks in the distribution.

Residual Sugar has two high pillars in the histogramm and the boxplot is indicating that they are quite an amount of outliers. I will add here for more information a log scale:

There seems to be a high amount of outliers explaining the large bar in the histogram. Looks like a long tailed distribution to me.

The distribution of free sulfur dioxide is right skewed, the amount of outliers is not high.

The distribution is positively skewed and there are a few outliers, but not that high ones.

This looks like an almost normal distribution, the outliers are found of both sides as well as the the whiskers of the box plot.

The pH level graph two tailed to me with some outliers on both sides.

The suphates are long tailes positive skewed and some outlier occur.

I am going to ignore the outliers in my investigation as I am not sure if their deletion is not influencing the analysis in a way which would be correct.

For alcohol, it appears highest rates wines (7-8) have the percentage above the median.

What is the structure of your dataset?

The dataset contains 13 variables and 1599 observations.

What is/are the main feature(s) of interest in your dataset?

I think the most obvious features are quality, alcohol rate and sugar level. The more advanced wine consumer might be also interested in ph socre and the other chemical features.

What other features in the dataset do you think will help support your
investigation into your feature(s) of interest?

The more advanced wine consumer might be also interested in ph socre and he other chemical features.

Did you create any new variables from existing variables in the dataset?

yes, I created a rating variable

Of the features you investigated, were there any unusual distributions?
Did you perform any operations on the data to tidy, adjust, or change the form
of the data? If so, why did you do this?

The alcohol rate and free sulfur dioxide are left skewed. Density and pH score are normally distributed. The alcohol content seems to vary from 8 to 14 with major peaks around 10 with a lower count between 13 and 14. The pH value seems to dispaly a normal distribution with major samples exhibiting values between 3.0 and 3.5 We find a normal distribution on the quality and the worst wine and the great one might be outliers. Most of the wines can be considered as average ones.

Bivariate Plots Section

As my/ Brain’s main interest is in the quality, it will be interesting to check the correlation of the variables espcially with quality. But it might be also be interesting to check the chemical variables against each other.

type.

type.

Pinky is watching Brain working hard ;-)

Pearson’s correlation coefficient is the test statistics that measures the statistical relationship, or association, between two continuous variables. It is known as the best method of measuring the association between variables of interest because it is based on the method of covariance. (source https://www.statisticssolutions.com/pearsons-correlation-coefficient/)

A graphic solution:

testing example for correlation with the pearson method

## 
##  Pearson's product-moment correlation
## 
## data:  rw$quality and rw$alcohol
## t = 21.639, df = 1597, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4373540 0.5132081
## sample estimates:
##       cor 
## 0.4761663
## 
##  Pearson's product-moment correlation
## 
## data:  rw$quality and rw$residual.sugar
## t = 0.5488, df = 1597, p-value = 0.5832
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.03531327  0.06271056
## sample estimates:
##        cor 
## 0.01373164
## 
## ---------------------------------------------------------------------------
##           &nbsp;            fixed.acidity   volatile.acidity   citric.acid 
## -------------------------- --------------- ------------------ -------------
##     **fixed.acidity**             1             -0.2561        **0.6717**  
## 
##    **volatile.acidity**        -0.2561             1           **-0.5525** 
## 
##      **citric.acid**         **0.6717**       **-0.5525**           1      
## 
##     **residual.sugar**         0.1148           0.001918         0.1436    
## 
##       **chlorides**            0.09371           0.0613          0.2038    
## 
##  **free.sulfur.dioxide**       -0.1538          -0.0105         -0.06098   
## 
##  **total.sulfur.dioxide**      -0.1132          0.07647          0.03553   
## 
##        **density**            **0.668**         0.02203        **0.3649**  
## 
##           **pH**             **-0.683**          0.2349        **-0.5419** 
## 
##       **sulphates**             0.183            -0.261        **0.3128**  
## 
##        **alcohol**            -0.06167          -0.2023          0.1099    
## 
##        **quality**             0.1241         **-0.3906**        0.2264    
## ---------------------------------------------------------------------------
## 
## Table: Table continues below
## 
##  
## ------------------------------------------------------------------------------
##           &nbsp;            residual.sugar   chlorides    free.sulfur.dioxide 
## -------------------------- ---------------- ------------ ---------------------
##     **fixed.acidity**           0.1148        0.09371           -0.1538       
## 
##    **volatile.acidity**        0.001918        0.0613           -0.0105       
## 
##      **citric.acid**            0.1436         0.2038          -0.06098       
## 
##     **residual.sugar**            1           0.05561            0.187        
## 
##       **chlorides**            0.05561           1             0.005562       
## 
##  **free.sulfur.dioxide**        0.187         0.005562             1          
## 
##  **total.sulfur.dioxide**       0.203          0.0474         **0.6677**      
## 
##        **density**            **0.3553**       0.2006          -0.02195       
## 
##           **pH**               -0.08565        -0.265           0.07038       
## 
##       **sulphates**            0.005527      **0.3713**         0.05166       
## 
##        **alcohol**             0.04208        -0.2211          -0.06941       
## 
##        **quality**             0.01373        -0.1289          -0.05066       
## ------------------------------------------------------------------------------
## 
## Table: Table continues below
## 
##  
## -----------------------------------------------------------------------------
##           &nbsp;            total.sulfur.dioxide     density         pH      
## -------------------------- ---------------------- ------------- -------------
##     **fixed.acidity**             -0.1132           **0.668**    **-0.683**  
## 
##    **volatile.acidity**           0.07647            0.02203       0.2349    
## 
##      **citric.acid**              0.03553          **0.3649**    **-0.5419** 
## 
##     **residual.sugar**             0.203           **0.3553**     -0.08565   
## 
##       **chlorides**                0.0474            0.2006        -0.265    
## 
##  **free.sulfur.dioxide**         **0.6677**         -0.02195       0.07038   
## 
##  **total.sulfur.dioxide**            1               0.07127      -0.06649   
## 
##        **density**                0.07127               1        **-0.3417** 
## 
##           **pH**                  -0.06649         **-0.3417**        1      
## 
##       **sulphates**               0.04295            0.1485        -0.1966   
## 
##        **alcohol**                -0.2057          **-0.4962**     0.2056    
## 
##        **quality**                -0.1851            -0.1749      -0.05773   
## -----------------------------------------------------------------------------
## 
## Table: Table continues below
## 
##  
## -------------------------------------------------------------------
##           &nbsp;            sulphates      alcohol       quality   
## -------------------------- ------------ ------------- -------------
##     **fixed.acidity**         0.183       -0.06167       0.1241    
## 
##    **volatile.acidity**       -0.261       -0.2023     **-0.3906** 
## 
##      **citric.acid**        **0.3128**     0.1099        0.2264    
## 
##     **residual.sugar**       0.005527      0.04208       0.01373   
## 
##       **chlorides**         **0.3713**     -0.2211       -0.1289   
## 
##  **free.sulfur.dioxide**     0.05166      -0.06941      -0.05066   
## 
##  **total.sulfur.dioxide**    0.04295       -0.2057       -0.1851   
## 
##        **density**            0.1485     **-0.4962**     -0.1749   
## 
##           **pH**             -0.1966       0.2056       -0.05773   
## 
##       **sulphates**             1          0.09359       0.2514    
## 
##        **alcohol**           0.09359          1        **0.4762**  
## 
##        **quality**            0.2514     **0.4762**         1      
## -------------------------------------------------------------------
sorry.

sorry.

after all this numbers, time for a little bit of fun :-)

Bivariate Analysis

Talk about some of the relationships you observed in this part of the
investigation. How did the feature(s) of interest vary with other features in
the dataset?

Alcohol has negative correlation with density. This is expected as alcohol is less dense than water.

Residual.sugar does not show correlation with quality. Free.sulfur.dioxide and total.sulfur.dioxide are highly correlated as expected.

Density has a very strong correlation with fixed.acidity.

Did you observe any interesting relationships between the other features
(not the main feature(s) of interest)?

Volatile.acidity has a positive correlation with pH. This is unexpected as pH is a direct measure of acidity.

What was the strongest relationship you found?

The variables that have the strongest correlations to quality are volatile.acidity and alcohol.

Multivariate Plots Section / Multivariate Analysis

Talk about some of the relationships you observed in this part of the
investigation. Were there features that strengthened each other in terms of
looking at your feature(s) of interest?

I think it makes sense to check first on the strongest correlation, which is the one between alcohol and volatile.acidity.

Hm, it looks as if alcohol rate is higher in good wines. But not yet any real pattern visible despite that.

Talk about some of the relationships you observed in this part of the
investigation. Were there features that strengthened each other in terms of
looking at your feature(s) of interest?

My feature of interst is the quality / ranking and what is influencing it. Therefore I am going to check not only the alcohol level but as well the variables for ph level and acid as well as residual sugar. I suspect there might be some relationship.

Next step to try is using the ph-level as this is realted with acidity.

I googled the relation between the ph- level and citirc acid: H3Citrate citiric acid, C6H8O7 (citirc acid) 3.24, I found that citric acid is used to regulate the ph- score (in cosmetic products) and is an anti- oxidant. (source: wikipedia) From my opinion this graph is underlining the correlation between the ph score and the citric acid addition to a wine.

The higher the density is, the darker is the residual sugar.

The picture shows a higher density is linked to a lower level of alcohol and a higher level of quality.

This chart shows how quality improves as the alcohol content increases and the volitile acidity decreases.The overall trend of the colors getting darker as they go to the bottom right. The second plot shows the relation of the rating with votile acidity and pH score. I was interested in the result because normally acidity and pH have a strong correlation.

Were there any interesting or surprising interactions between features?

The density decreases as the alcohol increases. What may not be as obvious is that the density increases as sugar increases, but it’s along the opposite direction. The median of the residual sugar lies parallel to the alcohol vs density trendline. Interesting as I had no knowledge about wine before doing this analyis.

Final Plots and Summary

Plot One

Description One

We see here the right skewed distribution of the alcohol and the number of wines, the mean (10,42%) and the median (10.2%) are realtively close.There are not many wines which have a percentage higher than 12%.

Plot Two

Description Two

The boxplot shown in the analysis above is devlivering less information than the scatterplot, but it is more compact and they can be easily compared. Here we can see that good wines have a higher level of alcohol. Both pictures are to be seen complementary to underline the correlation between quality / rating and alcohol content.

Plot Three

Description Three

Density of wine is strong. The higher the alcohol %, the lower is the density. It’s clearly visible that in this plot, stronger wine tend to have higher rating.

Reflection

As I do not know much about chemistry, my analysis is clearly limited and there is potential to take more care of the correlations between them. My analysis is concentrated clearly on quality / ranking - a variable I built, and the density and level of alcohol. It shows there is a strong link between them. According to my analyis a good wine has a level of alcohol around 10% and 10.5%. From the the final plot 1 we can say a good wine has a votile acidity 0.4. The additional checking of votile acidity and pH together with quality did not give much more insight.

I had a struggle with finding a good method of correlating the variables whereas the first steps went pretty ok. I did not pay much attention on outliers. All in all it was fun to step into something really new as I never had any touch with R. I learned that R is a very powerful tool in explanatory data analysis and give pretty surprising insights of an anknown topic.

The topic of building a model could be one of further investigation. I am not sure how to choose the best variables for a predictive model. On top my knowledge of wine is so limited that I would like to know which acids could explain the variation in the pH score as I think the three listed ones do not explain well enough.

In summarization it is interesting to have worked on the content of wine. I just want to state that the human taste shall not be underestimated :-) even though the rating is based on human taste.

night.

night.

And on top I hope you enjoy the little side story of Pinky and Brain which is my favourite comic show. When I am working together at work with a programmer we always make the joke that we are Pinky and Brain, never sure, who is who.